Explore your prediction results

After you set up Firebase Predictions, your users are dynamically segmented
into groups based on their predicted behavior. Firebase Predictions has a
broad spectrum of signals it can potentially use to make these predictions,
including Analytics events that are automatically logged and that you
explicitly logged, the user's device configuration, and basic attributes of your
app, such as its binary size. However, for any given prediction, only some of
the available signals are relevant.

As a hypothetical example, when predicting whether a user is likely to spend, a
user's recent spending behavior is probably very relevant, whereas its binary
size is probably not. However, in practice, the signals that actually have
predictive relevance are often surprising and can change over time.

To gain insight into exactly what data went into making your predictions, you
can explore your predictions in the Firebase console.

Open a prediction's details page

Ensure the app and platform you're interested in is selected from the
drop-down menu at the top of the console.

If you have predictions set up for the app you selected, you will see a page
that contains cards with information about the predefined predictions
and any custom predictions you might have created for the app.

On the card for the prediction you want to explore, click Select an
action > Explore this prediction.

The prediction details page opens.

What went into making this prediction?

Expand the What went into making this prediction section of the prediction
details page to see what data went into making the selected prediction over the
most recent 28-day period predictions are available (the exact timeframe is
indicated at the top-right of the page).

The signals shown in this section were all used to determine whether each of
your 28-day active users were predicted to perform the action in question. Also
shown are the average values and value range of these signals over all users
that were predicted to perform the action, given the risk tolerance you
selected. By selecting different risk tolerance levels, you can see how the
values of each signal affected prediction confidence.

While this page lists most of the signals the prediction models use, the models
are frequently updated and improved, so some signals might not yet be included.

The data that contribute to a prediction belong to four high-level categories:
user engagement metrics, event frequency, user properties, and app attributes.
Note that some predictions might not use any signals in a category, in which
event the category will have no items under it.

The following sections describe the signals that can appear under each category.

User engagement metrics

User engagement metrics measure how much users used your app over several
timeframes.

Active users

Whether a user was active during a given timeframe.

Users are considered active if they trigger an Analytics event that
indicates user activity at least once in a given timeframe. The most
common event that indicates activity is user_engagement.
Note that not all events indicate user activity—receiving a
notification, for example, doesn't necessarily mean the user was active
in the app. Also, custom Analytics events currently aren't
considered to indicate user activity, because Predictions can't
determine which of your custom events can only be triggered by an active
user.

Click Active users to see the percentage of users that were
active over each of several day-long and week-long intervals, out of all
users that were predicted, with the selected risk tolerance, to perform
the action.

For example, if you select "not_churn—low risk tolerance" and
the histogram indicates 97% active users one week ago, that means that
of all the users who are confidently predicted not to churn, 97% were
active last week.

Continuous active days in last 7 days

The number of continuous days a user was active in the last 7 days.

Continuous active weeks in last 4 weeks

The number of continuous weeks a user was active in the last 4 weeks.

Active days in last 7 days

The number of days in the last 7 days a user was active.

Active weeks in last 4 weeks

The number of weeks in the last 4 weeks a user was active.

Continuous inactive days in last 7 days

The number of continuous days a user was inactive in the last 7 days.

Continuous inactive weeks in last 4 weeks

The number of continuous weeks a user was inactive in the last 4 weeks.

Inactive days in last 7 days

The number of days in the last 7 days a user was inactive.

Inactive weeks in last 4 weeks

The number of weeks in the last 4 weeks a user was inactive.

Days since earliest active day

The number of days since a user first logged activity in your app.

Event frequency

Firebase Predictions can potentially use any of the Analytics events
your app logs to make predictions, whether the Firebase SDK automatically logs
the event or you explicitly log the event.

Each of the Analytics events shown contributed to the prediction. You can
click them to see the most common parameter values logged with the event and the
percentage of users that triggered the event over each of several day-long and
week-long intervals, out of all users that were predicted, with the selected
risk tolerance, to perform the action. If one of the intervals is grayed out,
for that time interval, too few users triggered the event for the event to be
relevant.

For example, if you select "not_churn—low risk tolerance" and the
histogram indicates 77% users triggered the spend_virtual_currency event one
week ago, that means that of all the users who are confidently predicted not to
churn, 77% triggered the spend_virtual_currency event last week.

User properties

Firebase Predictions uses properties of the user's device to make
predictions about the user's future behavior.

OS freshness

How recently the operating system on the user's device was updated.

The prediction details page shows the average OS freshness of all users
in the selected prediction and risk tolerance segment, measured on a
scale from 0.0 to 1.0.

App freshness

How recently the user upgraded to a new version of your app.

The prediction details page shows the average app freshness of all
users in the selected prediction and risk tolerance segment, measured on
a scale from 0.0 to 1.0.

User default language

The default language configured on the user's device.

App attributes

Firebase Predictions uses static attributes of your app to make
predictions about a user's future behavior.